Horizontal Decision Tree Learning from Very High Rate Data Streams

ABSTRACT

A mechanism is provided in a data processing system for distributed tree learning. A source processing instance distributes data record instances to a plurality of model update processing items. The plurality of model update processing items determine candidate leaf splitting actions in a decision tree in parallel based on the data record instances. The plurality of model update processing items send the candidate leaf splitting actions to a plurality of conflict resolve processing items. The plurality of conflict resolve processing items identifies conflict leaf splitting actions. The plurality of conflict resolve processing items applies tree structure changes to the decision tree in the plurality of model update processing items.

BACKGROUND

The present application relates generally to an improved data processingapparatus and method and more specifically to mechanisms for enablinghorizontal decision tree learning from extremely high rate data streams.

Big data is a term for data sets so large or complex that traditionaldata processing applications are inadequate. Challenges includeanalysis, capture, curation, search, sharing, storage, transfer,visualization, and information privacy. The term often refers simply tothe use of predictive analytics or other certain advanced methods toextract value from data, and seldom to a particular size of data set.

Stream computing is a critical topic of big data. Stream computing isaffected by the velocity, volume, veracity, and variety of data. Streamcomputing applications must address low latency of processing, highspeed of data flow, fine grained data granularity, and potentiallyunlimited data size. Scalability plays a key role in stream computingsystems. Scalability involves the capability of distributed computingand parallelism.

InfoSphere® Streams is a big data and stream computing system byInternational Business Machines Corporation. InfoSphere® Streams is anadvanced analytic platform that allows user-developed applications toquickly ingest, analyze and correlate information as it arrives fromthousands of real-time sources. The solution can handle very high datathroughput rates, up to millions of events or messages per second. TheInternet of Things (IoT) is the network of physical objects or “things”embedded with electronics, software, sensors, and connectivity to enableit to achieve greater value and service by exchanging data with themanufacturer, operator, other connected devices, or the cloud. Eachthing is uniquely identifiable through its embedded computing system butis able to interoperate within the existing Internet infrastructure. IoTproduces a large amount of data to be processed in real time or in batchmode.

Decision tree induction is one of the most popular and importantalgorithms in large scale machine learning, both in batch mode andstreaming mode big data systems. Parallelism is well-studied instreaming scenarios, but existing solutions are imperfect.

Streaming Parallel Decision Tree (SPDT) algorithm is ail attempt toaddress high data arrival rate. SPDT uses a distributed data compressedrepresentation (histogram) computation but uses a centralized modelupdate, which is a bottleneck. SPDT cannot scale out due to thehigh-cost model update computation,

Scalable Advanced Massive Online Analysis (SAMOA) is a framework formining big data streams. SAMOA uses a Vertical Hoeffding Tree (VHT) forclassification. VHT is a distributed streaming version of decision treestailored for sparse data. SAMOA provides a distributed model updatecomputation from one instance's point of view. SAMOA does not utilizethe instance level parallelism; therefore, it cannot handle high dataarrival rate. Massive Online Analysis (MOA) is an unscalable streamingdecision tree, MOA uses sequential data input and model updatecomputation.

SUMMARY

In one illustrative embodiment, a method, in a data processing system,is provided for distributed tree learning. The method comprisesdistributing, by a source processing instance, data record instances toa plurality of model update processing items. The method furthercomprises determining, by the plurality of model update processingitems, candidate leaf splitting actions in a decision tree in parallelbased on the data record instances. The method further comprisessending, by the plurality of model update processing items, thecandidate leaf splitting actions to a plurality of conflict resolveprocessing items. The method further comprises identifying, by theplurality of conflict resolve processing items, conflict leaf splittingactions. The method further comprises applying, by the plurality ofconflict resolve processing items, tree structure changes to thedecision tree in the plurality of model update processing items.

In other illustrative embodiments, a computer program product comprisinga computer useable or readable medium having a computer readable programis provided. The computer readable program, when executed on a computingdevice, causes the computing device to perform various ones of, andcombinations of, the operations outlined above with regard to the methodillustrative embodiment.

In yet another illustrative embodiment, a system/apparatus is provided.The system/apparatus may comprise one or more processors and a memorycoupled to the one or more processors. The memory may compriseinstructions which, when executed by the one or more processors, causethe one or more processors to perform various ones of, and combinationsof, the operations outlined above with regard to the method illustrativeembodiment.

These and other features and advantages of the present invention will bedescribed in, or will become apparent to those of ordinary skill in theart in view of, the following detailed description of the exampleembodiments of the present invention.

BRIEF DESCRIPTION OF THE DRAWINGS

The invention, as well as a preferred mode of use and further objectivesand advantages thereof will best be understood by reference to thefollowing detailed description of illustrative embodiments when read inconjunction with the accompanying drawings, wherein:

FIG. 1 is an example diagram of a multiple processor data processingsystem in which aspects of the illustrative embodiments may beimplemented;

FIG. 2 is an example block diagram of a data processing system chip inwhich aspects of the illustrative embodiments may be implemented;

FIG. 3 depicts a mechanism for vertical parallelism for decision treelearning;

FIG. 4 depicts a mechanism for horizontal parallelism for decision treelearning;

FIG. 5 shows a node split for decision tree learning;

FIG. 6 depicts a typical streaming decision tree algorithm;

FIG. 7A depicts a mechanism for horizontal parallelism for decision treelearning with conflict resolution in accordance with an illustrativeembodiment;

FIGS. 7B-7D show decision tree models processed by model updateprocessing items in accordance with an illustrative embodiment;

FIG. 8 depicts a logical view of horizontal parallelism for decisiontree learning with conflict resolution in accordance with anillustrative embodiment; and

FIG. 9 is a flowchart of operation of a mechanism for horizontalparallelism for decision tree learning with conflict resolution inaccordance with an illustrative embodiment

DETAILED DESCRIPTION

Real-world applications of big data stream processing present severalchallenges. Data arrival rate is high. For instances, global positioningsystem (GPS) applications consider one million GPS data instances persecond in a small scale connected vehicle platform. Also, the dataattribute number (feature dimension) can be large. For example,real-time text analytics consider ten thousand or more attributes. Theamount of data to consider can be unbounded with data arriving twentyfour hours a day and seven days a week.

The illustrative embodiments provide mechanisms for enabling horizontaldecision tree learning from extremely high rate data streams. In someapplications, such as connected car or vehicle-to-vehicle communicationscenarios, the attribute number is not large, but the data rate isextremely high. The mechanisms of the illustrative embodimentshorizontally parallelize the most computationally intensive part ofdecision tree learning from high data rate streams.

Before beginning the discussion of the various aspects of theillustrative embodiments, it should first be appreciated that throughoutthis description the term “mechanism” will be used to refer to elementsof the present invention that perform various operations, functions, andthe like. A “mechanism,” as the term is used herein, may be animplementation of the functions or aspects of the illustrativeembodiments in the form of an apparatus, a procedure, or a computerprogram product. In the case of a procedure, the procedure isimplemented by one or more devices, apparatus, computers, dataprocessing systems, or the like. In the case of a computer programproduct, the logic represented by computer code or instructions embodiedin or on the computer program product is executed by one or morehardware devices in order to implement the functionality or perform theoperations associated with the specific “mechanism.” Thus, themechanisms described herein may be implemented as specialized hardware,software executing on general purpose hardware, software instructionsstored on a medium such that the instructions are readily executable byspecialized or general purpose hardware, a procedure or method forexecuting the functions, or a combination of any of the above.

The present description and claims may make use of the terms “a,” “atleast one of,” and “one or more of” with regard to particular featuresand elements of the illustrative embodiments. It should be appreciatedthat these terms and phrases are intended to state that there is atleast one of the particular feature or element present in the particularillustrative embodiment, but that more than one can also be present.That is, these terms/phrases are not intended to limit the descriptionor claims to a single feature/element being present or require that aplurality of such features/elements be present. To the contrary, theseterms/phrases only require at least a single feature/element with thepossibility of a plurality of such features/elements being within thescope of the description and claims.

In addition, it should be appreciated that the following descriptionuses a plurality of various examples for various elements of theillustrative embodiments to further illustrate example implementationsof the illustrative embodiments and to aid in the understanding of themechanisms of the illustrative embodiments. These examples intended tobe non-limiting and are not exhaustive of the various possibilities forimplementing the mechanisms of the illustrative embodiments. It will beapparent to those of ordinary skill in the art in view of the presentdescription that there are many other alternative implementations forthese various elements that may be utilized in addition to, or inreplacement of, the examples provided herein without departing from thespirit and scope of the present invention.

The illustrative embodiments may be utilized in many different types ofdata processing environments. In order to provide a context for thedescription of the specific elements and functionality of theillustrative embodiments, FIGS. 1 and 2 are provided hereafter asexample environments in which aspects of the illustrative embodimentsmay be implemented. It should be appreciated that FIGS. 1 and 2 are onlyexamples and are not intended to assert or imply any limitation withregard to the environments in which aspects or embodiments of thepresent invention may be implemented. Many modifications to the depictedenvironments may be made without departing from the spirit and scope ofthe present invention.

FIG. 1 depicts a pictorial representation of an example distributed dataprocessing system in which aspects of the illustrative embodiments maybe implemented. Distributed data processing system 100 may include anetwork of computers in which aspects of the illustrative embodimentsmay be implemented. The distributed data processing system 100 containsat least one network 102, which is the medium used to pro videcommunication links between various devices and computers connectedtogether within distributed data processing system 100. The network 102may include connections, such as wire, wireless communication links, orfiber optic cables.

In the depicted example, server 104 and server 106 are connected tonetwork 102 along with storage unit 108. In addition, clients 110, 112,and 114 are also connected to network 102. These clients 110, 112, and114 may be, for example, personal computers, network computers, or thelike. In the depicted example, server 104 provides data, such as bootfiles, operating system images, and applications to the clients 110,112, and 114. Clients 110, 112, and 114 are clients to server 104 in thedepicted example. Distributed data processing system 100 may includeadditional servers, clients, and other devices not shown.

In the depicted example, distributed data processing system 100 is theInternet with network 102 representing a worldwide collection ofnetworks and gateways that use the Transmission ControlProtocol/Internet Protocol (TCP/IP) suite of protocols to communicatewith one another. At the heart, of the Internet is a backbone ofhigh-speed data communication lines between major nodes or hostcomputers, consisting of thousands of commercial, governmental,educational and other computer systems that route data and messages. Ofcourse, the distributed data processing system 100 may also beimplemented to include a number of different types of networks, such asfor example, an intranet, a local area network (LAN), a wide areanetwork (WAN), or the like. As stated above, FIG. 1 is intended as anexample, not as an architectural limitation for different embodiments ofthe present invention, and therefore, the particular elements shown inFIG. 1 should not be considered limiting with regard to the environmentsin which the illustrative embodiments of the present invention may beimplemented.

FIG. 2 is a block diagram of an example data processing system in whichaspects of the illustrative embodiments may be implemented. Dataprocessing system 200 is an example of a computer, such as client 110 inFIG. 1, in which computer usable code or instructions implementing theprocesses for illustrative embodiments of the present invention may belocated.

In the depicted example, data processing system 200 employs a hubarchitecture including north bridge and memory controller hub (NB/MCH)202 and south bridge and input/output (I/O) controller hub (SB/ICH) 204.Processing unit 206, main memory 208, and graphics processor 210 areconnected to NB/MCH 202. Graphics processor 210 may be connected toNB/MCH 202 through an accelerated graphics port (AGP).

In the depicted example, local area network (LAN) adapter 212 connectsto SB/ICH 204. Audio adapter 216, keyboard and mouse adapter 220, modem222, read only memory (RDM) 224, hard disk drive (HDD) 226, CD-ROM drive230, universal serial bus (USB) ports and other communication ports 232,and PCI/PCIe devices 234 connect to SB/ICH 204 through bus 238 and bus240. PCI/PCIe devices may include, for example, Ethernet adapters,add-in cards, and PC cards for notebook computers. PCI uses a card buscontroller, while PCIe does not. ROM 224 may be, for example, a flashbasic input/output system (BIOS).

HDD 226 and CD-ROM drive 230 connect to SB/ICH 204 through bus 240. HDD226 and CD-ROM drive 230 may use, for example, an integrated driveelectronics (IDE) or serial advanced technology attachment (SATA)interface. Super I/O (SIO) device 236 may be connected to SB/ICH 204.

An operating system runs on processing unit 206. The operating systemcoordinates and provides control of various components within the dataprocessing system 200 in FIG. 2. As a client, the operating system maybe a commercially available operating system such as Microsoft® Windows7®. An object-oriented programming system, such as the Java™ programmingsystem, may run in conjunction with the operating system and providescalls to the operating system from Java™ programs or applicationsexecuting on data processing system 200.

As a server, data processing system 200 may be, for example, an IBMeServer™ System p® computer system, Power™ processor based computersystem, or the like, running the Advanced Interactive Executive (AIX®)operating system or the LINUX® operating system. Data processing system200 may be a symmetric multiprocessor (SMP) system including a pluralityof processors in processing unit 206. Alternatively, a single processorsystem may be employed.

Instructions for the operating system, the object-oriented programmingsystem, and applications or programs are located on storage devices,such as HDD 226, and may be loaded into main memory 208 for execution byprocessing unit 206. The processes for illustrative embodiments of thepresent invention may be performed by processing unit 206 using computerusable program code, which may be located in a memory such as, forexample, main memory 208, ROM 224, or in one or more peripheral devices226 and 230, for example.

A bus system, such as bus 238 or bus 240 as shown in FIG. 2, may becomprised of one or more buses. Of course, the bus system may beimplemented using any type of communication fabric or architecture thatprovides for a transfer of data between different components or de vicesattached to the fabric or architecture. A communication unit, such asmodem 222 or network adapter 212 of FIG. 2, may include one or moredevices used to transmit and receive data. A memory may be, for example,main memory 208, ROM 224, or a cache such as found in NB/MCH 202 in FIG.2.

Those of ordinary skill in the art will appreciate that the hardware inFIGS. 1 and 2 may vary depending on the implementation. Other internalhardware or peripheral devices, such as flash memory, equivalentnon-volatile memory, or optical disk drives and the like, may be used inaddition to or in place of the hardware depicted in FIGS. 1 and 2. Also,the processes of the illustrative embodiments may be applied to amultiprocessor data processing system, other than the SMP systemmentioned previously, without departing from the spirit and scope of thepresent invention.

Moreover, the data processing system 200 may take the form of any of anumber of different data processing systems including client computingdevices, server computing devices, a tablet computer, laptop computer,telephone or other communication device, a personal digital assistant(PDA), or the like. In some illustrative examples, data processingsystem 200 may be a portable computing device that is configured withflash memory to provide non-volatile memory for storing operating systemfiles and/or user-generated data, for example, Essentially, dataprocessing system 200 may be any known or later developed dataprocessing system without architectural limitation.

FIG. 3 depicts a mechanism for vertical parallelism for decision treelearning, Source processing item 305 receives a plurality of datainstances 310. A processing item (PI) is a computer, processing node,processor, processing core, virtualized processing hardware, etc, in adistributed computing environment, such as distributed data processingsystem 100 in FIG. 1. Each data instance 310 includes a plurality ofattributes (A, B, C, D, etc.), Vertical parallelism is achieved bydistributing a subset of attributes to each of a plurality oflocal-statistic PIs 315. The mechanism aggregates local statistics to aglobal decision tree. Vertical parallelism is suitable when dimension(the number of attributes) is high. However, the parallelism level isbounded by O(#attributes) at one instance level, Vertical Hoeffding Tree(VHT) in Scalable Advanced Massive Online Analysis (SAMOA) follows thisparadigm type but processes only one instance at a time.

FIG. 4 depicts a mechanism for horizontal parallelism for decision treelearning. Source PI 405 receives a plurality of data instances 410.Horizontal parallelism is achieved by distributing data instances tolocal statistic PIs 415, which produce local statistics for decisiontree learning. Model aggregator PI 420 performs periodic local-statisticaggregation. Horizontal parallelism is suitable when data arrival rateis high. There is no bound for the parallelism level. Streaming ParallelDecision Tree (SPDT) follows this paradigm type; however, tree modellearning is centralized, which restricts overall scalability.

FIG. 5 shows a node split for decision tree learning. The decision treehas a root node that branches to a plurality of child nodes. Each nodebranches based on the value of an attribute until the tree reaches aleaf node, which determines the class of the instance of data. A treecan be “learned” by splitting the source set into subsets based on anattribute value test. This process is repeated on each derived subset ina recursive manner called recursive partitioning. The recursion iscompleted when the subset at a node has all the same value of the targetvariable, or when splitting no longer adds value to the predictions. Inthe depicted example, the node splits on the sample question,ATTR_ONE>5?

FIG. 6 depicts a typical streaming decision tree algorithm. Thealgorithm 600 is an example of Hoeffding tree induction, where E is atraining instance and HT is the current state of the decision tree. Insteps 4-8, for each attribute, algorithm 600 computes G ₁/(X_(l)), whichis the information gain for splitting on the attribute. In steps 6 and7, algorithm 600 finds the attribute with the highest G _(l) and theattribute with the second highest G _(l). The portion of the algorithm600 in steps 4-8 is the most computationally intensive part of decisiontree learning.

Decision tree learning (i.e., model update of leaf node splitting) isnot strictly horizontally parallelizable, because tree structurelearning is typically sensitive to instance order. In SPDT, thebottleneck of centralized model update restricts the horizontalparallelism level and overall scalability. The illustrative embodimentsare based on determining that instance order change in data streams mayresult in different tree structure, but the predictive performance maynot be sensitive to the order.

In an experiment, a mechanism generates 100,000 data record instancesusing a random tree. The first 50,000 are training samples, and the restare testing samples. The mechanism applies MOA to train a streamingdecision tree. The mechanism denotes the performance (correct predictionpercentage) by Baseline.

The mechanism then randomly assigns 50% of the instances as trainingsamples and the rest as testing samples to retrain the streamingdecision tree in a trail. The mechanism repeats five times to see if thepredictive performance changes. The results are as follows:Baseline=90.19%, Trial 1=90.25%, Trial 2=90.14%, Trial 3=90.14%, Trial4=90.28%, Trial 5=90.39%, and the average of the five trials=90.25%).The implication is that the instance order change in the data stream mayresult in a different tree structure, but the predictive performance isnot sensitive to the order if given the “independent and identicallydistributed” (i.i.d.) assumption. Most of the time, predictiveperformance is the goal of machine learning; the model structure is notthe goal. This allows the mechanisms of the illustrative embodiments todesign horizontal decision tree learning from streaming data.

FIG. 7A. depicts a mechanism for horizontal parallelism for decisiontree learning with conflict resolution in accordance with anillustrative embodiment. Source processing item (PI) 705 receives aplurality of data instances 710. Horizontal parallelism is achieved bydistributing data instances to model update PIs 715, which perform localdecision tree learning. In each model update PI 715, tree learning islocal. Such a concurrent learning paradigm is equivalent to applyingorder change to training instances. Each model update PI 715 computesinformation gain or other measures to obtain candidate leaf splittingactions in parallel. Each model update PI 715 determines candidate treenodes that must be split. In one example embodiment, model update PIs715 use a hash function to send identification of leaf nodes to conflictresolve PIs 720.

Conflict resolve PIs 720 detect conflicts. Conflict resolve PIs 720prioritize and decide which split action to take. Conflict resolve PIs720 mark “from_MUPI_id” of the blocked split action, Conflict resolvePIs 720 aggregate statistical information for the same leaves to ensureinformation consistency and applies tree changes back to model updatePIs 715.

FIGS. 7B-7D show decision tree models processed by model updateprocessing items in accordance with an illustrative embodiment. FIG. 7Bshows a decision tree model processed by model update PI A; FIG. 7Cshows a decision tree model processed by model update PI B; and, FIG, 7Dshows a decision tree model processed by model update PI C. The decisiontree model in FIG. 7B generates statistical information for leaf nodesA1, A2; the decision tree model in FIG. 7C generates statisticalinformation for leaf nodes B1, B2; and, the decision tree model in FIG.7D generates statistical information for leaf nodes C1, C2.

Each model update PI 715 has access to a decision tree replica or hasaccess to the decision tree in shared memory. Model update PIs 715perform the following function:

Map<leaf_id, (splt_attr_id, splt_point, stat_info, from_MUPI_id)>

where leaf_id is the identification of a leaf node, such as A1, A2 inFIG. 7B; the splt_attr_id is the identification of an attribute causingthe split; the splt_point is the attribute value at which the splitoccurs; the stat_info is the statistical information such as informationgain; and, from_MUPI_id is the identification of the model update PI. Inthe depicted example, conflict resolve PIs 720 identify a conflictinvolving leaf node C2 and decides not to take the leaf splitting actionassociated with leaf node C2.

In the next round, model update PIs 715 whose identifier is the markedfrom_MUPI_id do not read new data but keep the old data batch to computeinformation gain as in normal cases. The only difference is that thenodes that are split in the last round will not accept instances in thedata batch again, referred to as “closing valves” for these nodes. Thisis to prevent the same data from being learned multiple times at thesame nodes.

As the tree grows larger, different small sets of instances become moreand more improbable to fall into a same leaf node. This means that theprobability of observing conflicts decreases over time. In practice, astreaming decision tree can have thousands (10³ or more) of leaf nodes,but only a very small portion will be split in each cycle (10⁰˜10¹).

FIG. 8 depicts a logical view of horizontal parallelism for decisiontree learning with conflict resolution in accordance with anillustrative embodiment. To determine the scalability advantage,consider the following: D is the data size arrived in a unit of time,d_(i) is the parallelism level of the model update PI, d_(c) is theparallelism level of the conflict resolve PI, n_(tree) is the number ofnodes of the current decision tree, n_(attr) is the number ofattributes, L is the number of leaves that stat_info is changed, U isthe number of leaves that are finally split for the model update, and Tis the average computational time of information gain for one attribute,which can be large in practice.

The sequential computation complexity for one cycle is as follows:

D·O(log n_(tree))+L·n _(attr) ·T

For SPDT, the computation complexity is as follows:

${\frac{1}{d_{i}}{D \cdot {O\left( {\log n}_{tree} \right)}}} + {L \cdot n_{attr} \cdot T}$

For the mechanisms of the illustrative embodiments, the worst case ofthe computational complexity is as follows:

${\frac{1}{d_{i}}\left\lbrack {{D \cdot {O\left( {\log n}_{tree} \right)}} + {L \cdot n_{attr} \cdot T}} \right\rbrack} + {\frac{1}{d_{c}}{O\left( {L \cdot d_{i}} \right)}}$

For the mechanisms of the illustrative embodiments, the best case of the

computational complexity is as follows:

${\frac{1}{d_{i}}\left\lbrack {{D \cdot {O\left( {\log n}_{tree} \right)}} + {L \cdot n_{attr} \cdot T}} \right\rbrack} + {\frac{1}{d_{c}}{O(L)}}$

The present invention may be a system, a method, and/or a computerprogram product. The computer program product may include a computerreadable storage medium (or media) having computer readable programinstructions thereon for causing a processor to carry out aspects of thepresent invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area, network,a wide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent, instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as Java, Smalltalk, C++ or the like,and conventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions may execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computeror entirely on the remote computer or server. In the latter scenario,the remote computer may be connected to the user's computer through anytype of network, including a local area network (LAN) or a wide areanetwork (WAN), or the connection may be made to an external computer(for example, through the Internet using an Internet Service Provider).In some embodiments, electronic circuitry including, for example,programmable logic circuitry, field-programmable gate arrays (FPGA), orprogrammable logic arrays (PLA) may execute the computer readableprogram instructions by utilizing state information of the computerreadable program instructions to personalize the electronic circuitry,in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

FIG. 9 is a flowchart of operation of a mechanism for horizontalparallelism for decision tree learning with conflict resolution inaccordance with an illustrative embodiment. Operation begins (block900), and the mechanism horizontally partitions streaming instances andfeeds data record instances to distributed model update processinginstances (block 901). Each model update processing instance (PI)computes information gain or other measures to obtain candidate leafsplitting actions in parallel (block 902).

The mechanism aggregates all candidate leaf splitting actions inconflict resolve PIs (block 903). The conflict resolve PIs detectconflict actions, prioritizes, and decides which actions to take (block904), The conflict resolve PIs mark the model update PI identifier(from_MUPI_id) of the blocked split action (block 905). The conflictresolve PIs aggregate local statistical information from ail candidateleaf splitting actions from model update PIs in conflict resolve PIs(block 906). The conflict resolve PIs then apply the tree change(structure and statistical information) to the tree model (block 907).

The mechanism determines whether the end of the data stream is reached(block 908), If the end of the data stream is reached, then operationends (block 909), If the end of the data stream is not reached in block908, then operation returns to block 901. In the next round of the datafeed, the model update PIs whose identifiers are the marked from_MUPI_iddo not read new data but keep the old data batch to compute informationgain as in normal cases. The only difference is that the nodes that aresplit in the last round will not accept instance in the data batchagain, referred to as “closing valves” for these nodes. This is toprevent the same data from being learned for multiple times at the samenodes.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

As noted above, it should be appreciated that the illustrativeembodiments may take the form of an entirely hardware embodiment, anentirely software embodiment or an embodiment containing both hardwareand software elements. In one example embodiment, the mechanisms of theillustrative embodiments are implemented in software or program code,which includes but is not limited to firmware, resident software,microcode, etc.

A data processing system suitable for storing and/or executing programcode will include at least one processor coupled directly or indirectlyto memory elements through a system bus. The memory elements can includelocal memory employed during actual execution of the program code, bulkstorage, and cache memories which provide temporary storage of at leastsome program code in order to reduce the number of times code must beretrieved from bulk storage during execution.

Input/output or I/O devices (including but not limited to keyboards,displays, pointing devices, etc.) can be coupled to the system eitherdirectly or through intervening I/O controllers. Network adapters mayalso be coupled to the system to enable the data processing system tobecome coupled to other data processing systems or remote printers orstorage devices through intervening private or public networks. Modems,cable modems and Ethernet cards are just a few of the currentlyavailable types of network adapters.

The description of the present invention has been presented for purposesof illustration and description, and is not intended to be exhaustive orlimited to the invention in the form disclosed. Many modifications andvariations will be apparent to those of ordinary skill in the artwithout departing from the scope and spirit of the describedembodiments. The embodiment was chosen and described in order to bestexplain the principles of the invention, the practical application, andto enable others of ordinary skill in the art to understand theinvention for various embodiments with various modifications as aresuited to the particular use contemplated. The terminology used hereinwas chosen to best explain the principles of the embodiments, thepractical application or technical improvement over technologies foundin the marketplace, or to enable others of ordinary skill in the art tounderstand the embodiments disclosed herein.

1-10. (canceled)
 11. A computer program product comprising a computerreadable storage medium having a computer readable program storedtherein, wherein the computer readable program, when executed on acomputing device, causes the computing device to: distribute, by asource processing instance, data record instances to a plurality ofmodel update processing items; determine, by the plurality of modelupdate processing items, candidate leaf splitting actions in a decisiontree in parallel based on the data record instances; send, by theplurality of model update processing items, the candidate leaf splittingactions to a plurality of conflict resolve processing items; identify,by the plurality of conflict resolve processing items, conflict leafsplitting actions; and apply, by the plurality of conflict resolveprocessing items, tree structure changes to the decision tree in theplurality of model update processing items.
 12. The computer programproduct of claim 11, wherein determining candidate leaf splittingactions comprises computing information gain for candidate leafsplitting actions.
 13. The computer program product of claim 12, whereindetermining candidate leaf splitting actions further comprisesdetermining a first attribute having a highest information gain and asecond attribute having a second highest information gain.
 14. Thecomputer program product of claim 11, wherein each of the plurality ofmodel update processing items has access to a replica of the decisiontree.
 15. The computer program product of claim 11, wherein each of theplurality of model update processing items has access to the decision,tree in shared memory.
 16. The computer program product of claim 11,wherein sending the candidate leaf splitting actions comprises sending aleaf identifier, an attribute identifier for an attribute causing a leafsplit, a split point at which the leaf splits, local statisticalinformation, and an identifier of fee model update processing item. 17.The computer program product of claim 11, wherein the computer readableprogram causes the computing device to: determine, by the plurality ofconflict resolve processing items for a given candidate leaf split,whether to take the given candidate leaf split or to block the givencandidate leaf split.
 18. The computer program product of claim 11,wherein the computer readable program causes the computing device to:aggregate, by the plurality of conflict resolve processing items, localstatistical information for each candidate leaf split from the firstplurality of model update processing items.
 19. The computer programproduct of claim 11, wherein the computer readable program causes thecomputing device to: communicate, by the plurality of conflict resolveprocessing items, blocked candidate leaf split actions.
 20. The computerprogram product of claim 19, wherein a given model update processingitem matching an identifier of a blocked candidate leaf split actiondoes not read new data in a next cycle.
 21. An apparatus comprising: aprocessor; and a memory coupled to the processor, wherein the memorycomprises instructions which, when executed by the processor, cause theprocessor to: distribute, by a source processing instance, data recordinstances to a plurality of model update processing items; determine, bythe plurality of model update processing items, candidate leaf splittingactions in a decision tree in parallel based on the data recordinstances; send, by the plurality of model update processing items, thecandidate leaf splitting actions to a plurality of conflict resolveprocessing items; identify, by the plurality of conflict resolveprocessing items, conflict leaf spitting actions; and apply, by theplurality of conflict resolve processing items, tree structure changesto the decision tree in the first plurality of model update processingitems.